We use PyTorch [1]} library to implement our method, which is trained with 4 NVIDIA RTX 3090 GPUs.
The size of the range image is set to \(64 \times 2048\) .
We apply the same data augmentation as used in LMNet [2]} during training.
We minimize \(\mathcal {L}_{wce}\)  and \(\mathcal {L}_{ls}\)  using the stochastic gradient descent with momentum 0.9 and weight decay 0.0001. The initial learning rate is set to 0.01. We use the implementation of 3D sparse convolution from TorchSparse [3]} to implement our PointHead.
We train the network using a two-stage training scheme. First, we train the 2D convolutional network with the image labels. After that, we freeze the parameters of the 2D encoder-decoder network and use the point cloud labels to train the PointHead separately.
